Beware of R(2): Simple, Unambiguous Assessment of the Prediction Accuracy of QSAR and QSPR Models.
Beware of R(2): Simple, Unambiguous Assessment of the Prediction Accuracy of QSAR and QSPR Models.
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DOI：
10.1021/acs.jcim.5b00206
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发表时间：
2015-07-27
影响因子：
5.6
通讯作者：
Winkler DA
中科院分区：
文献类型：
--
作者：
Alexander DL;Tropsha A;Winkler DA
用于表征模型的外部预测性的统计度量，即它对独立测试集的属性的预测程度，在过去十年中已经激增。本文澄清了在QSAR和QSPR建模中使用决定系数R2作为模型拟合和预测能力度量的一些明显混淆。R2（或R2）在文献中与训练和测试数据一起用于各种上下文，用于普通线性回归和通过原点的回归以及线性和非线性回归模型。我们用严格的统计方法分析了被广泛采用的Golbraikh和Tropsha提出的模型拟合准则。指出了这些标准的缺点，并提供了一种更清晰、更简单的替代方法来表征模型的预测性。其目的不是重复使用测试数据进行模型验证的充分论证，而是指导R2作为模型拟合统计量的应用。通过示例说明R2的正确和错误用法。报告均方根误差或等效的离散度测量，通常比R2更具有实际重要性，也受到鼓励，并概述了解决不同类别用户（如计算化学家、实验科学家和监管决策支持专家）需求的重要挑战。
The statistical metrics used to characterize the external predictivity of a model, i.e., how well it predicts the properties of an independent test set, have proliferated over the past decade. This paper clarifies some apparent confusion over the use of the coefficient of determination, R2, as a measure of model fit and predictive power in QSAR and QSPR modelling. R2 (or R2) has been used in various contexts in the literature in conjunction with training and test data, for both ordinary linear regression and regression through the origin as well as with linear and nonlinear regression models. We analyze the widely adopted model fit criteria suggested by Golbraikh and Tropsha in a strict statistical manner. Shortcomings in these criteria are identified and a clearer and simpler alternative method to characterize model predictivity is provided. The intent is not to repeat the well-documented arguments for model validation using test data, but to guide the application of R2 as a model fit statistic. Examples are used to illustrate both correct and incorrect use of R2. Reporting the root mean squared error or equivalent measures of dispersion, typically of more practical importance than R2, is also encouraged and important challenges in addressing the needs of different categories of users such as computational chemists, experimental scientists, and regulatory decision support specialists are outlined.